Title: Cache Orchestration and Optimization in IoT Networks
Jury Members:
| Mme AnnaMaria VEGNI, Roma Tree University, Italy | Rapporteur | ||||
| M. Antoine GALLAIS, INSA Hauts-de-France, France | Rapporteur | ||||
| M. Jamshid BAGHERZADEH, Urmia University, Iran | Examinateur | ||||
| Mme Leila SHARIFI, Urmia University, Iran | Examinatrice | ||||
| M. Vahid SOLOUK, Urmia University, Iran | Examinateur | ||||
| M. Yezekael HAYEL, Avignon University, France | Examinateur | ||||
| M. Abderrahim BENSLIMANE, Avignon University, France | Direteur de thèse | ||||
| M. Saleh YOUSEFI, Urmia University, Iran | Co-Direteur de thèse | ||||
Abstract
In the rapidly evolving landscape of the Internet of Things (IoT) and the Internet of Vehicles (IoV), caching emerges as a pivotal technology to enhance network efficiency, reduce latency, and improve user experiences. These technological domains face growing demands for better data management and delivery mechanisms due to increasing data volumes and network complexity.
In this thesis, we explore innovative caching strategies within the realms of the IoT and the IoV to enhance network services and user experiences. The research presented spans three distinct yet interconnected studies, each addressing critical aspects of network performance, including latency reduction, content delivery efficiency, and network coverage expansion.
The first study focuses on enhancing content-centric networking caching capabilities within IoT environments. By employing hierarchical network orchestrations and a global SDN/Cache controller (GSCC), our approach centralizes cache decisions, optimizing resource usage across the network. Devices are grouped into clusters with heads acting as cache controllers, streamlining content delivery and reducing transmission hops significantly. We introduce Multi-Criteria Decision Making (MCDM) techniques such as Analytical Hierarchy Process (AHP) and TOPSIS, which evaluate multiple metrics to optimize caching decisions. Simulation results from this study show an average cache hit rate of 72%, a reduction in hop counts by 42%, and the maintenance of operational functionality across 60% of nodes during 90% of the simulation period.
The second study delves into the challenges posed by the high mobility of vehicles in IoV networks. We propose two specialized caching methods tailored to distinct content types prevalent in IoV: safety and infotainment. The Federated Learning-based Mobility-aware Collaborative Content Caching (FM3C) method is designed for infotainment, while the Spatio-Temporal Characteristics Aware Emergency Content Caching (STAECC) method caters to emergency data. We utilize federated learning to predict content popularity while preserving user privacy, integrated with multi-criteria decision-making to optimize cache placement in Road-Side Units (RSUs). Our innovative approach not only enhances cache hit rates to up to 98% but also significantly reduces content retrieval delays to under 10ms, showcasing an efficient provision of IoV content.
The final study introduces an innovative UAV deployment strategy termed SONA, which supports caching and extends network coverage, particularly in IoV scenarios where RSUs are overwhelmed or absent. Utilizing blockchain technology for decentralized network orchestration, SONA applies a Pareto Optimization approach and transforms the deployment challenge into a Markov Decision Process (MDP), solved using Deep Reinforcement Learning (DRL). This strategy not only reduces average latency to less than 8ms but also achieves higher user satisfaction rates with fewer UAVs deployed, demonstrating the effectiveness of dynamic UAV positioning and caching based on predictive analytics.
Together, these studies illustrate a progression from centralized caching and network management towards more autonomous, decentralized, and intelligent network systems.